DESCRIPTION: (Applicant's Description) With the invention of hybridoma technology by Kohler and Milstein (1975), numerous monoclonal antibodies (MoAbs) against cell surface antigens or receptors have been developed and used clinically as diagnostic agents. In the last two decades there has been enormous effort in both academia and pharmaceutical industry to develop monoclonal antibodies to treat human cancers. The recent clinical success of Rituxan (anti-CD 20 MoAb against B-cell lymphoma) and Herceptin (anti-Her2/neu MoAb against breast cancer) in the treatment of human cancers has validated the cell-surface targeting approach for cancer therapy. Evaluation of biopsy specimens for the presence of CD20 and Her2/neu is now routinely done for non-Hodgkin lymphoma and breast cancer, respectively. Combinatorial chemistry has become one of the most important technologic advances in recent years for basic research and drug discovery. Millions of compounds can be generated and screened for their ability to bind to a specific target macromolecule or to elicit a specific biological response (Lam 1997). In this proposal, we hypothesize that by using the state-of-the-art "one-bead one-compound" combinatorial library method, we can rapidly identify a large number of cell surface binding peptides that are unique to different tumor types. We further hypothesize that with the novel chemical microarray technique that was recently developed in our laboratory, we can rapidly characterize the binding specificities as well as functional effects of the identified peptide ligands on a large number of tumor cell lines. Peptides that are unique to human tumors can then be used to determine the ligand binding profile of human cancer biopsy specimens.